Thank you for checking out my kernel!

Please upvote if you like the dashboard and if you have any suggestions feel free to leave a comment!

.

---
title: "Suicide Dashboard" 
# author: "Saba Tavoosi"
# date: "June 13, 2019"
output: 
  flexdashboard::flex_dashboard:
    source_code: embed
    vertical_layout: scroll
    theme: yeti
---

Thank you for checking out my kernel!  

Please upvote if you like the dashboard and if you have any suggestions feel free to leave a comment!

.

``` {js}
// Inverse color of navigation bar.
$('.navbar-inverse').removeClass('navbar-inverse').addClass('navbar-default');
```

```{r setup, include=FALSE}
# Load necessary packages. 
library(flexdashboard) # Dashboard package
library(highcharter) # Interactive data visualizations
library(plotly) # Interactive data visualizations
library(viridis) # Color gradients
library(tidyverse) # Metapackge
library(countrycode) # Converts country names/codes
library(rjson) # JSON reader
library(crosstalk) # Provides interactivity for HTML widgets
library(DT) # Displaying data tables
```

```{r include=FALSE}
# Read in data. 
data <- read.csv('./master.csv')%>%
  filter(year!= 2016,country != 'Dominica',
         country != 'Saint Kitts and Nevis')
# Fix the names of some of the countries in our data to match the country names 
# used by our map later on so that they'll be interpreted and displayed. 
data <- data %>%
  mutate(country = fct_recode(country, "The Bahamas" = "Bahamas"),
         country = fct_recode(country, "Cape Verde" = "Cabo Verde"),
         country = fct_recode(country, "South Korea" = "Republic of Korea"),
         country = fct_recode(country, "Russia" = "Russian Federation"),
         country = fct_recode(country, "Republic of Serbia" = "Serbia"),
         country = fct_recode(country, "United States of America" = "United States"))

# Reorder levels of age to be in chronological order. 
data$age <- factor(data$age, levels = c("5-14 years", "15-24 years", "25-34 years", "35-54 years", "55-74 years", "75+ years"))
```

```{r include=FALSE}
# Create a custom theme for the plots. 
custom_theme <- hc_theme(
  colors = c('#5CACEE', 'green', 'red'),
  chart = list(
         backgroundColor = '#FAFAFA', 
         plotBorderColor = "black"),
  xAxis = list(
         gridLineColor = "E5E5E5", 
         labels = list(style = list(color = "#333333")), 
         lineColor = "#E5E5E5", 
         minorGridLineColor = "#E5E5E5", 
         tickColor = "#E5E5E5", 
         title = list(style = list(color = "#333333"))), 
  yAxis = list(
         gridLineColor = "#E5E5E5", 
         labels = list(style = list(color = "#333333")), 
         lineColor = "#E5E5E5", 
         minorGridLineColor = "#E5E5E5", 
         tickColor = "#E5E5E5", 
         tickWidth = 1, 
         title = list(style = list(color = "#333333"))),   
  title = list(style = list(color = '#333333', fontFamily = "Lato")),
  subtitle = list(style = list(color = '#666666', fontFamily = "Lato")),
  legend = list(
         itemStyle = list(color = "#333333"), 
         itemHoverStyle = list(color = "#FFF"), 
         itemHiddenStyle = list(color = "#606063")), 
  credits = list(style = list(color = "#666")),
  itemHoverStyle = list(color = 'gray'))
```




Worldwide
=======================================================================

 Column {.tabset .tabset-fade data-width=700 .colored }
-----------------------------------------------------------------------

### Worldwide suicides {.no-padding}
```{r fig.height=5}
# Create tibble for our line plot.  
overall_tibble <- data %>%
  select(year, suicides_no, population) %>%
  group_by(year) %>%
  summarise(suicide_capita = round((sum(suicides_no)/sum(population))*100000, 2)) 

# Create a line plot.
highchart() %>% 
    hc_add_series(overall_tibble, hcaes(x = year, y = suicide_capita, color = suicide_capita), type = "line") %>%
    hc_tooltip(crosshairs = TRUE, borderWidth = 1.5, headerFormat = "", pointFormat = paste("Year: {point.x} 
 Suicides: {point.y}")) %>%
    hc_title(text = "Worldwide suicides by year") %>% 
    hc_subtitle(text = "1985-2015") %>%
    hc_xAxis(title = list(text = "Year")) %>%
    hc_yAxis(title = list(text = "Suicides per 100K people"),
             allowDecimals = FALSE,
             plotLines = list(list(
                    color = "black", width = 1, dashStyle = "Dash", 
                    value = mean(overall_tibble$suicide_capita),
                    label = list(text = "Mean = 13.12", 
                                 style = list(color = "black", fontSize = 11))))) %>%
    hc_legend(enabled = FALSE) %>% 
    hc_add_theme(custom_theme)
```

### Worldwide suicides by Gender {.no-padding}
```{r fig.height=5.14}
# Create tibble for sex so we can use it when creating our line plot.  
sex_tibble <- data %>%
  select(year, sex, suicides_no, population) %>%
  group_by(year, sex) %>%
  summarise(suicide_capita = round((sum(suicides_no)/sum(population))*100000, 2))

# Pick color for gender.
sex_color <- c("#EE6AA7", "#87CEEB") # baby blue & pink

# Create line plot.
highchart() %>% 
    hc_add_series(sex_tibble, hcaes(x = year, y = suicide_capita, group = sex), type = "line", color = sex_color) %>%
    hc_tooltip(crosshairs = TRUE, borderWidth = 1.5, headerFormat = "", pointFormat = paste("Year: {point.x} 
","Gender: {point.sex}
", "Suicides: {point.y}")) %>%
    hc_title(text = "Worldwide suicides by Gender") %>% 
    hc_subtitle(text = "1985-2015") %>%
    hc_xAxis(title = list(text = "Year")) %>%
    hc_yAxis(title = list(text = "Suicides per 100K people"),
             allowDecimals = FALSE,
             plotLines = list(list(
                    color = "black", width = 1, dashStyle = "Dash",
                    value = mean(overall_tibble$suicide_capita),
                    label = list(text = "Mean = 13.12", 
                                 style = list(color = 'black', fontSize = 11))))) %>% 
    hc_add_theme(custom_theme)
```

### Worldwide suicides by Age {.no-padding}
```{r fig.height=5.13}
# Create tibble for age so we can use it when creating our line plot.  
age_tibble <- data %>%
  select(year, age, suicides_no, population) %>%
  group_by(year, age) %>%
  summarise(suicide_capita = round((sum(suicides_no)/sum(population))*100000, 2))

# Pick color for graph. 
age_color <- rev(plasma(6))

# Create a line plot.
highchart() %>% 
    hc_add_series(age_tibble, hcaes(x = year, y = suicide_capita, group = age), type = "line", color = age_color) %>%
    hc_tooltip(crosshairs = TRUE, borderWidth = 1.5, headerFormat = "", pointFormat = paste("Year: {point.x} 
","Age: {point.age}
", "Suicides: {point.y}")) %>%
    hc_title(text = "Worldwide suicides by Age") %>% 
    hc_subtitle(text = "1985-2015") %>%
    hc_xAxis(title = list(text = "Year")) %>%
    hc_yAxis(title = list(text = "Suicides per 100K people"),
             allowDecimals = FALSE,
             plotLines = list(list(
                    color = "black", width = 1, dashStyle = "Dash",
                    value = mean(overall_tibble$suicide_capita),
                    label = list(text = "Mean = 13.12", 
                                 style = list(color = 'black', fontSize = 11))))) %>% 
    hc_add_theme(custom_theme)
```


Column {data-width=300}
-----------------------------------------------------------------------

### Suicides per 100K  (1985-2015) 
```{r fig.height=0.8}
# Grab worldwide number of suicides per 100K people from the data
total_suicides <- round(mean(overall_tibble$suicide_capita), 2)

# Create value box
valueBox(total_suicides, icon = "fa-plus", color = 'firebrick')
```

### Worldwide suicides by Gender {.no-title .no-padding .colored }
```{r fig.height=1.9}
# First, make a tibble of suicide by sex. We will use this for our pie chart.
pie_sex <- data %>%
  select(sex, suicides_no, population) %>%
  group_by(sex) %>%
  summarise(suicide_capita = round((sum(suicides_no)/sum(population))*100000, 2))
  
# Create pie chart for sex. 
highchart() %>% 
  hc_add_series(pie_sex, hcaes(x = sex, y = suicide_capita, 
                               color = sex_color), type = "pie") %>%
  hc_tooltip(borderWidth = 1.5, headerFormat = "", pointFormat = paste("Gender: {point.sex} ({point.percentage:.1f}%) 
 Suicides per 100K: {point.y}")) %>%
  hc_title(text = "Worldwide suicides by Gender", style = (list(fontSize = '14px'))) %>% 
  hc_subtitle(text = "1985-2015", style = (list(fontSize = '10px'))) %>%
  hc_plotOptions(pie = list(dataLabels = list(distance = 5, 
                            style = list(fontSize = 10)), 
                            size = 130)) %>% 
  hc_add_theme(custom_theme)
```

### Worldwide suicides by Age {.no-title .no-padding .colored }
```{r fig.height=1.9}
# First, create a tibble of suicide by Age. We will use this for our pie chart.
pie_age <- data %>%
  select(age, suicides_no, population) %>%
  group_by(age) %>%
  summarise(suicide_capita = round((sum(suicides_no)/sum(population))*100000, 2)) %>%
  arrange(suicide_capita)

# Create pie chart for Age. 
highchart() %>% 
  hc_add_series(pie_age, hcaes(x = age, y = suicide_capita, 
                               color = age_color), type = "pie") %>%
  hc_tooltip(borderWidth = 1.5, headerFormat = "", pointFormat = paste("Age: {point.age} ({point.percentage:.1f}%) 
 Suicides per 100K: {point.y}")) %>%  
  hc_title(text = "Worldwide suicides by Age", style = (list(fontSize = '14px'))) %>% 
  hc_subtitle(text = "1985-2015", style = (list(fontSize = '10px'))) %>%
  hc_plotOptions(pie = list(dataLabels = list(distance = 5, 
                            style = list(fontSize = 10)), 
                            size = 130)) %>% 
  hc_add_theme(custom_theme)
```



Continents 
========================================================================

Column {data-width=375}
-----------------------------------------------------------------------

```{r include=FALSE}
# Create new column in our data for continent. Use countrycode() to extract continents from country names. 
data$continent <- countrycode(sourcevar = data$country,
                              origin = "country.name",
                              destination = "continent")

# Reclassify countries that have been coded as 'Americas', by countrycode(), into 'North America' and 'South America'. 
south_america <- c('Argentina', 'Brazil', 'Chile', 'Colombia', 'Ecuador', 'Guyana', 'Paraguay', 'Suriname', 'Uruguay')

data$continent[data$country %in% south_america] <- 'South America'
data$continent[data$continent=='Americas'] <- 'North America'
```

### Suicides by continent and Gender {.no-title .no-padding .colored }
```{r fig.height=2.35}
# Create a tibble for continent and sex.
continent_sex_tibble <- data %>%
  select(continent, sex, suicides_no, population) %>%
  group_by(continent, sex) %>%
  summarize(suicide_capita = round((sum(suicides_no)/sum(population))*100000, 2))

# Create histogram of suicides by continent.
highchart() %>%
hc_add_series(continent_sex_tibble, hcaes(x = continent, y = suicide_capita, group = sex), type = "column")  %>% 
    hc_colors(colors = sex_color) %>%
    hc_title(text = "Suicides by continent and Gender", style = (list(fontSize = '14px'))) %>% 
    hc_subtitle(text = "1985-2015") %>%
    hc_tooltip(borderWidth = 1.5, pointFormat = paste("Gender:  {point.sex}  
 Suicides: {point.y}")) %>%
    hc_xAxis(categories = c("Africa", "Asia", "Europe", "North 
 America", "Oceania", "South 
 America"), labels = list(style = list(fontSize = 8))) %>%
    hc_yAxis(labels = list(style = list(fontSize = 10)),
             title = list(text = "Suicides per 100K people",
             style = list(fontSize = 10)),
        plotLines = list(
          list(color = "black", width = 1, dashStyle = "Dash", 
               value = mean(overall_tibble$suicide_capita),
               label = list(text = "Mean = 13.12", style = list(color = "black", fontSize = 6))))) %>%     
    hc_legend(verticalAlign = 'top', enabled = FALSE) %>% 
    hc_add_theme(custom_theme)
```

### Suicides by continent and Age {.no-title .no-padding .colored }
```{r fig.height=2.35}
# Create a tibble for continent and sex.
continent_age_tibble <- data %>%
  select(continent, age, suicides_no, population) %>%
  group_by(continent, age) %>%
  summarize(suicide_capita = round((sum(suicides_no)/sum(population))*100000, 2)) 

# Create histogram of suicides by continent.
highchart() %>%
hc_add_series(continent_age_tibble, hcaes(x = continent, y = suicide_capita, group = age), type = "column")  %>% 
    hc_colors(colors = age_color) %>%
    hc_title(text = "Suicides by continent and Age", style = (list(fontSize = '14px'))) %>% 
    hc_subtitle(text = "1985-2015") %>%
    hc_tooltip(borderWidth = 1.5, pointFormat = paste("Age:  {point.age}  
 Suicides: {point.y}")) %>%
    hc_xAxis(categories = c("Africa", "Asia", "Europe", "North 
 America", "Oceania", "South 
 America"), labels = list(style = list(fontSize = 8))) %>%
    hc_yAxis(labels = list(style = list(fontSize = 10)),
             title = list(text = "Suicides per 100K people",
                          style = list(fontSize = 10)),
        plotLines = list(
          list(color = "black", width = 1, dashStyle = "Dash", 
               value = mean(overall_tibble$suicide_capita),
               label = list(text = "Mean = 13.12", style = list(color = "black", fontSize = 6))))) %>%    
    hc_legend(verticalAlign = 'top', enabled = FALSE) %>% 
    hc_add_theme(custom_theme)
```


Column {data-width=625}
-----------------------------------------------------------------------

### Suicides by continent {.no-title .no-padding .colored }
```{r}
# Import continent map data. 
# In order for the map to work on kaggle I had to save the map data and import it. 
# This is what is shown as my "private data set". If you want to reproduce this in R you can use the 
# commented out code right below it which downloads the map data straight from the internet. 
# map_data <- fromJSON(file = "../input/world-continents-geo-json/world-continents.geo.json")
map_data <- download_map_data("custom/world-continents")

# Create a tibble for continent.
continent_tibble <- data %>%
  select(continent, suicides_no, population) %>%
  group_by(continent) %>%
  summarize(suicide_capita = round((sum(suicides_no)/sum(population))*100000, 2)) %>%
  arrange(suicide_capita)

# Create continent map with suicide data. 
highchart() %>%
hc_add_series_map(map_data, continent_tibble, value = "suicide_capita", joinBy = c('name','continent'), name = "Suicides (per 100K people)")  %>% 
    hc_add_series(continent_tibble, hcaes(x = continent, y = suicide_capita, color = suicide_capita), type = "pie", name = 'Suicides (per 100K people)')  %>% 
    hc_colorAxis(stops = color_stops()) %>% 
    hc_title(text = "Suicides by Continent") %>% 
    hc_subtitle(text = "1985-2015") %>%
    hc_tooltip(borderWidth = 1.5, valueSuffix = '') %>%
    hc_plotOptions(
    pie = list(center = c('10%', '80%'), size = 110, dataLabels = list(enabled = FALSE))) %>% 
    hc_add_theme(custom_theme)
```



Countries
========================================================================

Column {.tabset .tabset-fade data-width=400 .colored }
-----------------------------------------------------------------------

### By country {.no-padding}
```{r fig.height=4.55}
# Create tibble for overall suicides by country
country_bar <- data %>%
  select(country, suicides_no, population) %>%
  group_by(country) %>%
  summarise(suicide_capita = round((sum(suicides_no)/sum(population))*100000, 2)) %>%
  arrange(desc(suicide_capita))

# Create interactive bar plot
highchart() %>%
    hc_add_series(country_bar, hcaes(x = country, y = suicide_capita, color = suicide_capita), type = "bar")  %>% 
    hc_tooltip(borderWidth = 1.5, 
               pointFormat = paste("Suicides: {point.y}")) %>%
    hc_legend(enabled = FALSE) %>%
    hc_title(text = "Suicides by country") %>% 
    hc_subtitle(text = "1985-2015") %>%
    hc_xAxis(categories = country_bar$country, 
             labels = list(step = 1),
             min = 0, max = 25,
             scrollbar = list(enabled = TRUE)) %>%
    hc_yAxis(title = list(text = "Suicides per 100K people")) %>%
    hc_plotOptions(bar = list(stacking = "normal", 
                              pointPadding = 0, groupPadding = 0, borderWidth = 0.5)) %>% 
    hc_add_theme(custom_theme)
```

### By gender {.no-padding}
```{r fig.height=4.65}
# Create tibble for suicide by countries and sex. 
country_bar_sex <- data %>%
  select(country, sex, suicides_no, population) %>%
  group_by(country, sex) %>%
  summarise(suicide_capita = round((sum(suicides_no)/sum(population))*100000, 2))

country_tibble <- data %>%
  select(country, suicides_no, population) %>%
  group_by(country) %>%
  summarise(suicide_capita = round((sum(suicides_no)/sum(population))*100000, 2)) 

# Create bar chart of suicide by sex.
highchart() %>%
    hc_add_series(country_bar_sex, hcaes(x = country, y = suicide_capita, group = sex), type = "bar", color = sex_color)  %>% 
    hc_tooltip(borderWidth = 1.5, pointFormat = paste("Gender: {point.sex} ({point.percentage:.1f}%) 
 Suicides per 100K: {point.y}")) %>%
    hc_legend(enabled = TRUE, colorByPoint = TRUE) %>%
    hc_title(text = "Suicides by country and gender") %>% 
    hc_subtitle(text = "1985-2015") %>%
    hc_xAxis(categories = country_tibble$country,
             labels = list(step = 1),
             min = 0, max = 25,
             scrollbar = list(enabled = TRUE)) %>%
    hc_yAxis(title = list(text = "Percentage of total suicides")) %>%
    hc_plotOptions(bar = list(stacking = "percent", 
                              pointPadding = 0, groupPadding = 0, borderWidth = 0.4)) %>% 
    hc_add_theme(custom_theme)
```

### By age {.no-padding}
```{r fig.height=4.65}
# Create tibble for suicide by countries and age 
country_bar_age <- data %>%
  select(country, age, suicides_no, population) %>%
  group_by(country, age) %>%
  summarise(suicide_capita = round((sum(suicides_no)/sum(population))*100000, 2))

# Create interactive bar plot.
highchart() %>%
    hc_add_series(country_bar_age, hcaes(x = country, y = suicide_capita, group = age), type = "bar", color = age_color)  %>% 
    hc_tooltip(borderWidth = 1.5, pointFormat = paste("Age: {point.age} ({point.percentage:.1f}%) 
 Suicides per 100K: {point.y}")) %>%
    hc_title(text = "Suicides by country and age") %>% 
    hc_subtitle(text = "1985-2015") %>%
    hc_xAxis(categories = country_tibble$country,
             labels = list(step = 1),
             min = 0, max = 25,
             scrollbar = list(enabled = TRUE)) %>%
    hc_yAxis(title = list(text = "Percent of total suicides")) %>%
    hc_plotOptions(bar = list(stacking = "percent", 
                              pointPadding = 0, groupPadding = 0, borderWidth = 0.5)) %>% 
    hc_add_theme(custom_theme)
```


Column {data-width=600}
-----------------------------------------------------------------------

### Suicides by country {.no-title .no-padding .colored }
```{r fig.height=4.9}
# Create a tibble with suicide per capita by country for 1985-2015. 
country_tibble <- data %>%
  select(country, suicides_no, population) %>%
  group_by(country) %>%
  summarize(suicide_capita = round((sum(suicides_no)/sum(population))*100000, 2))

# Create interactive world map.
highchart() %>%
hc_add_series_map(worldgeojson, country_tibble, value = "suicide_capita", joinBy = c('name','country'))  %>% 
    hc_colorAxis(stops = color_stops()) %>% 
    hc_title(text = "Suicides by Country") %>% 
    hc_subtitle(text = "1985-2015") %>%
    hc_tooltip(borderWidth = 1.5, headerFormat = "", valueSuffix = " suicides (per 100K people)") %>% 
    hc_add_theme(custom_theme)
```



Search {data-icon="fa-search"}
=======================================================================

Column {data-width=250}
-----------------------------------------------------------------------

### Filters {.no-title .colored }

**Pick filters here:**
```{r}
# Create tibble for our line plot.  
country_year_tibble <- data %>%
  select(country, year, suicides_no, population) %>%
  group_by(country, year) %>%
  summarise(suicide_capita = round((sum(suicides_no)/sum(population))*100000, 2)) 

# Create shared data that will be used to link filters, data table, and line plot. 
shared_data <- SharedData$new(country_year_tibble, group = 'hello')

# Create filter for year and country. These filters will adjust the DT datatable and PLOTLY plot. 
filter_slider("year", "Year", shared_data, ~year, step = 1)
filter_select("country", "Country", shared_data, ~country, allLevels = TRUE, multiple = TRUE)
```

***

**Data table**
```{r}
# Create datatable. 
datatable(shared_data,
          rownames = FALSE,
          colnames = c('Country', 'Year', 'Suicides /100K'),
          class = 'cell-border stripe',
          extensions = "Scroller",
          options=list(deferRender = FALSE, 
                       scrollY = 200, 
                       scrollCollapse = TRUE,
                       scroller = TRUE,
                       dom = 't'))
```


Column {data-width=750}
-----------------------------------------------------------------------

### Suicides by country {.no-title .colored }
```{r}
# Set a random seed. We will do this so that we can reproduce the random sample of colors we will use for our next graph. 
set.seed(80085)

# Create line graph. 
plot_ly(shared_data, x = ~year, y = ~suicide_capita, 
       color = ~country, colors = sample(colours(), 120),
       type = 'scatter', mode = 'lines',
       hoverinfo = 'text', text = ~paste("Country: ", country, '
Year: ', year, "
Suicides: ", suicide_capita)) %>%
       layout(showlegend = FALSE,
              title = "Suicide by country",
              xaxis = list(title = "Year"),
              yaxis = list(title = "Suicides per 100K people")) %>%
       layout(plot_bgcolor = 'transparent') %>% 
       layout(paper_bgcolor = 'transparent') %>% 
       add_markers() %>% 
       highlight("plotly_click")
```



About {data-icon="fa-info-circle"}
=======================================================================

Column {data-width=600}
-----------------------------------------------------------------------

### { .colored }

**Thank you for checking out my kernel!**  

Please **upvote** if you like the dashboard and if you have any suggestions feel free to leave a comment!




**Suicide Prevention:**

This wikipedia page lists suicide crisis lines by country:

* https://en.wikipedia.org/wiki/List_of_suicide_crisis_lines 




**References:**  

The data set used to create the dashboard can be found at:   

* https://www.kaggle.com/russellyates88/suicide-rates-overview-1985-to-2016 

and was compiled from data from the following sources: 

* United Nations Development Program. (2018). Human development index (HDI). Retrieved from http://hdr.undp.org/en/indicators/137506

* World Bank. (2018). World development indicators: GDP (current US$) by country:1985 to 2016. Retrieved from http://databank.worldbank.org/data/source/world-development-indicators#

* [Szamil]. (2017). Suicide in the Twenty-First Century [dataset]. Retrieved from https://www.kaggle.com/szamil/suicide-in-the-twenty-first-century/notebook

* World Health Organization. (2018). Suicide prevention. Retrieved from http://www.who.int/mental_health/suicide-prevention/en/